--- title: "ai-serving vs react-native-executorch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-software-mansion-react-native-executorch" tools: ["autodeployai-ai-serving", "software-mansion-react-native-executorch"] --- # ai-serving vs react-native-executorch *GraphCanon updated Aug 25, 2026* ## Verdict Pick ai-serving if ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker; pick react-native-executorch if react Native Executorch powers on-device AI inference in React Native apps using a declarative approach with ExecuTorch. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [react-native-executorch](http://docs.swmansion.com/react-native-executorch/) has 1.7k stars, 95 forks, and 64 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [react-native-executorch's repository](https://github.com/software-mansion/react-native-executorch). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [react-native-executorch](/tools/software-mansion-react-native-executorch.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Declarative way to run AI models in React Native on device | | Stars | 166 | 1,696 | | Forks | 31 | 95 | | Open issues | 3 | 64 | | Language | Scala | C++ | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | React Native Executorch powers on-device AI inference in React Native apps using a declarative approach with ExecuTorch. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving | Computer Vision, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [react-native-executorch](/tools/software-mansion-react-native-executorch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 171d | 0d | | Open issues (now) | 3 | 64 | | Stars delta | 0 (30d) | +23 (30d) | | Open issues delta | 0 (30d) | -13 (30d) | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/software-mansion-react-native-executorch/trust.md) | ## Decision facts: ai-serving - **Adopt for:** Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. ## Decision facts: react-native-executorch - **Adopt for:** React Native Executorch powers on-device AI inference in React Native apps using a declarative approach with ExecuTorch. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; react-native-executorch is C++. - License: ai-serving is Apache-2.0, react-native-executorch is Other. - Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx. - When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### Choose react-native-executorch if… - react-native-executorch is primarily C++; ai-serving is Scala. - License: react-native-executorch is Other, ai-serving is Apache-2.0. - Tags unique to react-native-executorch: image-embeddings, llm-inference, object-detection, ocr. - Also covers Computer Vision. - You are developing a React Native application requiring efficient on-device AI inference capabilities. ## When NOT to use ai-serving - Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. - Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. - If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ## When NOT to use react-native-executorch - When the development team lacks expertise in C++ or React Native integration specifics required by Executorch. - Projects that depend heavily on cloud-based machine learning services for their AI functionalities. - Scenarios where cross-platform support beyond React Native is necessary. ## Common questions ### What is the difference between ai-serving and react-native-executorch? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. react-native-executorch: Declarative way to run AI models in React Native on device. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over react-native-executorch? Choose ai-serving over react-native-executorch when ai-serving is primarily Scala; react-native-executorch is C++; License: ai-serving is Apache-2.0, react-native-executorch is Other; Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### When should I choose react-native-executorch over ai-serving? Choose react-native-executorch over ai-serving when react-native-executorch is primarily C++; ai-serving is Scala; License: react-native-executorch is Other, ai-serving is Apache-2.0; Tags unique to react-native-executorch: image-embeddings, llm-inference, object-detection, ocr; Also covers Computer Vision; You are developing a React Native application requiring efficient on-device AI inference capabilities. ### When should I avoid ai-serving? Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ### When should I avoid react-native-executorch? When the development team lacks expertise in C++ or React Native integration specifics required by Executorch. Projects that depend heavily on cloud-based machine learning services for their AI functionalities. Scenarios where cross-platform support beyond React Native is necessary. ### Is ai-serving or react-native-executorch more popular on GitHub? react-native-executorch has more GitHub stars (1,696 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and react-native-executorch open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, react-native-executorch: Other). ### Where can I find alternatives to ai-serving or react-native-executorch? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [react-native-executorch alternatives](/tools/software-mansion-react-native-executorch/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [react-native-executorch markdown twin](/tools/software-mansion-react-native-executorch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/autodeployai-ai-serving-vs-software-mansion-react-native-executorch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or react-native-executorch? ai-serving: Slowing. react-native-executorch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ai-serving and react-native-executorch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [react-native-executorch trust report](/tools/software-mansion-react-native-executorch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autodeployai-ai-serving`](/api/graphcanon/graph?tool=autodeployai-ai-serving) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_